A Framework for Email and Image Spam Detection for Improving Web Quality

Email spam is a major problem for the sustainability of the Internet and global commerce. Every day million of emails are sent across by spammers to targeted population to advertise products, services, spread dangerous software etc. Currently a number of spam detection algorithms have been proposed in the literature to classify email spam. Most of these algorithms can be categorized as metadata based, content based, behaviour based, etc. The existing literature has heavily focussed on advancing one or the other kind of approach i.e. there are a lot of algorithms doing content based or behaviour based detection, however there is not much work done that evaluates the effect of applying different algorithms in a step by step and/or iterative manner to achieve optimised classification. This research would aim to evaluate existing algorithms and propose a spam detection framework to automatically choose the correct algorithm sequence to do the classification based upon intelligently identified heuristics from the email profile.